x̄ - > Data of sales for SocialEntreprenuer nook from November 2019 to October 2023

Here is the plot of the monthly data of sales for SocialEntreprenuer nook  from November 2019 to October 2023. The graph highlights fluctuations and trends in the values over this period. Value is in (USD)

Key trends and patterns:

1. High Values:

   • The highest value recorded is 128.06 in April 2021.
   • Other significant peaks include 76.57 in December 2020, 70.52 in March 2021, and 116.49 in May 2022.

2. Zero Values:

   • There are several months with a value of 0, indicating no activity or measurement during those periods. Notable stretches of zero values include:
     • November 2019, January 2020, April 2020, June 2020, and several months in 2022 and 2023.

3. Seasonal Trends:

   • There seems to be a pattern of higher values towards the end of the year, particularly in the months of October, November, and December.
   • The mid-year months (June, July, August) often show lower values or zeros.

4. Yearly Comparison:

   • 2019-2020: Generally low values with occasional spikes (e.g., July 2020 with 66.69).
   • 2020-2021: Significant increase in values, especially from November 2020 to April 2021.
   • 2021-2022: High variability with peaks in September and October 2021.
   • 2022-2023: Mostly low values with occasional spikes (e.g., March 2022 with 87.83).

The code to generate the graph.

import pandas as pd

import matplotlib.pyplot as plt

# Defining the data

data = {

    "Date": [

        'Nov-19', 'Dec-19', 'Jan-20', 'Feb-20', 'Mar-20', 'Apr-20', 'May-20', 'Jun-20', 'Jul-20', 'Aug-20', 

        'Sep-20', 'Oct-20', 'Nov-20', 'Dec-20', 'Jan-21', 'Feb-21', 'Mar-21', 'Apr-21', 'May-21', 'Jun-21',

        'Jul-21', 'Aug-21', 'Sep-21', 'Oct-21', 'Nov-21', 'Dec-21', 'Jan-22', 'Feb-22', 'Mar-22', 'Apr-22', 

        'May-22', 'Jun-22', 'Jul-22', 'Aug-22', 'Sep-22', 'Oct-22', 'Nov-22', 'Dec-22', 'Jan-23', 'Feb-23',

        'Mar-23', 'Apr-23', 'May-23', 'Jun-23', 'Jul-23', 'Aug-23', 'Sep-23', 'Oct-23'

    ],

    "Value": [

        0, 3.5, 0, 5.5, 5, 0, 5.9, 0, 66.69, 4.58, 7.25, 12.16, 42.54, 76.57, 7.73, 16.95, 70.52, 128.06,

        24.45, 49.57, 19.37, 32.35, 86.63, 97.72, 3.49, 1, 0, 7.74, 87.83, 24.45, 116.49, 5.11, 0, 0, 0,

        2.92, 0, 0, 1, 0, 0, 1.1, 0, 0, 3, 0, 0, 1.1

    ]

}

# Creating a DataFrame

df = pd.DataFrame(data)

# Converting the Date column to datetime for better plotting

df['Date'] = pd.to_datetime(df['Date'], format='%b-%y')

# Plotting the data

plt.figure(figsize=(10, 6))

plt.plot(df['Date'], df['Value'], marker='o', linestyle='-', color='b')

plt.title('Monthly Data (Nov 2019 - Oct 2023)', fontsize=14)

plt.xlabel('Date', fontsize=12)

plt.ylabel('Value', fontsize=12)

plt.grid(True)

plt.xticks(rotation=45)

plt.tight_layout()

# Show plot

plt.show()

This work is licensed under a Creative Commons Attribution 4.0 International License.

Editor: Zacharia Maganga Nyambu

Email: zachariamaganga@duck.com